System and method for genomic association

Abstract

In variants, a method for genomic association can include: determining observed variable values and observed phenotype values for each organism in a population, removing information from variables of interest, determining a phenotype-variable association model, identifying causal variables associated with a phenotype, and/or any other suitable steps.

We claim: 
     
       1. A method, comprising:
 a) for each of a set of organisms:
 determining a trait value for a trait; and 
 determining variable values for a set of variables; 
 
 b) selecting a subset of variables from the set of variables; and 
 c) determining a first model configured to predict values for variables of interest in the set of variables based on values for the subset of variables; 
 d) determining test variable values for the variables of interest using the first model; 
 e) using the test variable values, determining a second model comprising a relationship between the set of variables and the trait; 
 f) determining an observed model metric for the second model using variable values for the subset of variables; 
 g) determining a test model metric for the second model using the test variable values; 
 h) identifying causal variables from the set of variables based on a comparison between the observed model metric and the test model metric; 
 i) determining target values for the causal variables based on a target trait; and 
 j) based on the target values, breeding organisms in the set of organisms to generate a new organism with the target trait. 
 
     
     
       2. The method of  claim 1 , further comprising training the first model using values for a second set of variables, different from the subset of variables. 
     
     
       3. The method of  claim 2 , wherein the first model comprises an autoencoder. 
     
     
       4. The method of  claim 1 , wherein determining the first model comprises a fitting a linear regression based on the variable values for the set of variables. 
     
     
       5. The method of  claim 1 , further comprising clustering the set of variables based on autocorrelation analysis of the variable values, wherein the subset of variables and variables of interest are from a shared cluster. 
     
     
       6. The method of  claim 5 , wherein variables in the set of variables comprise k-mers. 
     
     
       7. The method of  claim 1 , wherein (b)-(c) are iteratively repeated until a model fit metric for the first model rises above a threshold. 
     
     
       8. The method of  claim 1 , wherein a size of the subset of variables is less than a size of the set of organisms. 
     
     
       9. The method of  claim 1 , further comprising:
 determining a new instance of the first model; 
 determining new test variable values for the variables of interest using the new instance of the first model; and 
 determining a test model metric based on the new test variable values; wherein the causal variables are further identified based on a comparison between the observed model metric and the new test model metric. 
 
     
     
       10. The method of  claim 1 , wherein variables in the set of variables comprise variables for at least one of: loci, gene expression, protein expression, methylation, environmental parameters, or protein binding affinity.